Integrated Computational Approaches and Tools for Allosteric Drug Discovery

Abstract

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

Allosteric inhibition explained through conformational ensembles sampling distinct “mixed” states

“…Hence, allosteric modulation is a promising approach for eliciting selective and potent enzyme inhibition. This realization has prompted a plethora of studies for understanding allosteric communication and networks [3] , [4] , [5] , allosteric drug design [6] , [7] , [8] , [9] , as well as the mechanisms of allosteric inhibitors and partial agonists. The latter typically target the allosteric domains of enzymes such as kinases [10] , [11] , [12] , guanine nucleotide exchange factors [13] , [14] , tyrosine kinases [15] , [16] , and proteases [17] , [18] , or protein–protein interfaces such as molecular chaperone-client interactions [19] .…”

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

“…One class of such hotspots involves residues forming allosteric networks capable of inducing a shift in populations of protein conformations to support their altered function upon mutation. Here, we would like to highlight the availability of tools for rapid analyses of protein allostery focusing on residue-residue interactions in a single static structure or employing normal mode analysis (NMA) to approximate protein dynamics [134]. However, the performances of these approximate tools are often impeded by two factors: (i) the quality of a single-input structure and the extent, to which this structure represents essential interactions present in the conformational ensemble, and (ii) the limited sensitivity of underlying NMA to mutations that do not produce substantial conformational changes [135].…”

Targeting the C-Terminal Domain Small Phosphatase 1

“…The allosteric binding sites are identified through the literature and database searches, cavity finding, blind docking, coevolution, and so forth. Optimized allosteric binding sites are obtained from analysis and optimization, with the help of molecular dynamics simulation through perturbation response scanning, trajectory analysis, course-grained residue network analysis, and calculating Gibbs free energy [144]. Allosteric docking involves similar docking tools and algorithms to the other docking techniques summarized in Table 2.…”